Environmental pollution is among the most prominent challenges facing the world today, as its impact extends to air, water, soil, public health, and biodiversity. Monitoring it through traditional methods based on field sampling is increasingly difficult, because these methods require considerable time, effort, and cost, and cover only limited areas. Hence, remote sensing has emerged as a modern tool that enables the environment to be observed over wide areas, repeatedly, and on a regular basis. Remote sensing is the science of collecting information about the Earth’s surface and atmosphere without direct contact with the target, using sensors mounted on satellites, aircraft, or drones. These sensors work by measuring the electromagnetic radiation reflected or emitted by objects, since every material has a distinctive spectral signature that helps identify its properties and composition. In the field of air pollution, satellite sensors can detect the concentrations of major pollutants such as nitrogen dioxide, sulfur dioxide, carbon monoxide, and suspended particulates, as well as greenhouse gases such as methane and carbon dioxide. These data are used to identify pollution hotspots, track the movement of dust, dust storms, and wildfire smoke, and estimate the impact of industrial activities and transport on air quality. As for water pollution, remote sensing makes it possible to monitor indicators of surface water quality, such as turbidity, chlorophyll-a concentration (an indicator of algal blooms), suspended matter, and water surface temperature. Oil spills at sea can also be detected using synthetic aperture radar, which operates day and night and under cloudy conditions, in addition to tracking pollution resulting from wastewater and agricultural and industrial activities in rivers, lakes, and coastal areas. Remote sensing also contributes to monitoring land degradation, salinization, and land-use change, identifying the locations of illegal dumpsites, and tracking the effects of mining and oil activities. Soil contamination can be inferred from its effect on vegetation health using spectral indices such as the Normalized Difference Vegetation Index. Thermal imagery is also used to monitor urban heat islands and the thermal discharge from power plants and factories into water bodies. This field relies on a variety of platforms and technologies, including satellites such as the Landsat, Sentinel, and MODIS series, which provide multispectral data with global and periodic coverage; drones, which offer high spatial resolution and operational flexibility; and hyperspectral sensors, which capture hundreds of spectral bands and allow pollutants to be distinguished with greater accuracy. This is complemented by geographic information systems, which analyze data spatially, produce maps, and track changes over time, as well as by artificial intelligence and machine learning techniques, which process vast quantities of images, extract patterns, and predict the evolution of pollution. Among the most notable advantages of this technology are its coverage of large areas that are difficult to access on the ground, repeated monitoring that allows temporal changes to be studied, reduced cost and time compared with comprehensive field surveys, early warning and rapid response to environmental disasters such as oil spills and fires, and the provision of a historical data archive that supports long-term studies and decision-making. Nevertheless, this technology faces certain limitations, including the effect of clouds and weather conditions on image quality, the limited spatial or temporal resolution of some sensors, and the need to calibrate results and compare them with ground measurements to verify their accuracy. Some pollutants are also difficult to detect directly from space, and their analysis requires technical expertise and specialized software. For this reason, remote sensing is regarded as a complement to field measurements rather than a complete substitute for them. The future points toward further development, with the launch of small satellite constellations that provide near-daily coverage at high resolution, the integration of data with ground sensor networks and the Internet of Things, and the expanding use of artificial intelligence in the automatic detection and prediction of pollutants. This will enhance the ability of environmental agencies to make decisions based on accurate data in near real time. In conclusion, remote sensing is an effective tool for monitoring environmental pollution, given the comprehensive and continuously updated view it offers of the condition of air, water, and soil. Although it does not replace field measurements and ground verification, its integration with geographic information systems and artificial intelligence technologies makes it a fundamental pillar in environmental management, pollution reduction, public health protection, and sustainable development.